noise_energy.py 3.19 KB
 Philipp Arras committed Nov 21, 2017 1 2 3 4 5 ``````from .. import Field, exp from ..operators.diagonal_operator import DiagonalOperator from ..sugar import generate_posterior_sample from ..minimization.energy import Energy from ..utilities import memo `````` Philipp Arras committed Nov 21, 2017 6 7 8 9 10 11 12 13 14 15 `````` class NoiseEnergy(Energy): def __init__(self, position, d, m, D, t, FFT, Instrument, nonlinearity, alpha, q, Projection, samples=3, sample_list=None, inverter=None): super(NoiseEnergy, self).__init__(position=position.copy()) dummy = self.position.norm() self.m = m self.D = D self.d = d `````` Martin Reinecke committed Nov 22, 2017 16 `````` self.N = DiagonalOperator(diagonal=exp(self.position)) `````` Philipp Arras committed Nov 21, 2017 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 `````` self.t = t self.samples = samples self.FFT = FFT self.Instrument = Instrument self.nonlinearity = nonlinearity self.alpha = alpha self.q = q self.Projection = Projection self.power = self.Projection.adjoint_times(exp(0.5 * self.t)) self.one = Field(self.position.domain, val=1.) if sample_list is None: sample_list = [] if samples is None: sample_list.append(self.m) else: for i in range(samples): sample = generate_posterior_sample(m, D) sample = FFT(Field(FFT.domain, val=( FFT.adjoint_times(sample).val))) sample_list.append(sample) self.sample_list = sample_list self.inverter = inverter def at(self, position): return self.__class__(position, self.d, self.m, self.D, self.t, self.FFT, self.Instrument, self.nonlinearity, self.alpha, self.q, self.Projection, sample_list=self.sample_list, samples=self.samples, inverter=self.inverter) @property @memo def value(self): likelihood = 0. for sample in self.sample_list: likelihood += self._likelihood(sample) return ((likelihood / float(len(self.sample_list))) + 0.5 * self.one.vdot(self.position) + (self.alpha - self.one).vdot(self.position) + self.q.vdot(exp(-self.position))) def _likelihood(self, m): residual = self.d - \ self.Instrument(self.nonlinearity( self.FFT.adjoint_times(self.power * m))) energy = 0.5 * residual.vdot(self.N.inverse_times(residual)) return energy.real @property @memo def gradient(self): likelihood_gradient = Field(self.position.domain, val=0.) for sample in self.sample_list: likelihood_gradient += self._likelihood_gradient(sample) return (likelihood_gradient / float(len(self.sample_list)) + 0.5 * self.one + (self.alpha - self.one) - self.q * (exp(-self.position))) def _likelihood_gradient(self, m): residual = self.d - \ self.Instrument(self.nonlinearity( self.FFT.adjoint_times(self.power * m))) gradient = - 0.5 * \ self.N.inverse_times(residual.conjugate() * residual) return gradient @property @memo def curvature(self): pass``````